fit_control -> control_parsnip

Author: topepo

NAMESPACE

@@ -32,8 +32,8 @@ S3method(predict_raw,"_elnet")
 S3method(predict_raw,"_lognet")
 S3method(predict_raw,"_multnet")
 S3method(print,boost_tree)
+S3method(print,control_parsnip)
 S3method(print,decision_tree)
-S3method(print,fit_control)
 S3method(print,linear_reg)
 S3method(print,logistic_reg)
 S3method(print,mars)
@@ -90,6 +90,7 @@ export(C5.0_train)
 export(add_rowindex)
 export(boost_tree)
 export(check_empty_ellipse)
+export(control_parsnip)
 export(decision_tree)
 export(fit)
 export(fit.model_spec)

R/boost_tree.R

@@ -99,7 +99,7 @@
 #'  reloaded and reattached to the `parsnip` object.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()], [set_engine()]
+#' @seealso [[fit()], [set_engine()]
 #' @examples
 #' boost_tree(mode = "classification", trees = 20)
 #' # Parameters can be represented by a placeholder:

R/fit_control.R renamed to R/control_parsnip.R

@@ -16,18 +16,25 @@
 #'  class "try-error".
 #' @return An S3 object with class "fit_control" that is a named list with the
 #' results of the function call
+#' @details
+#' `fit_control()` is deprecated in favor of `control_parsnip()`.
 #' @export
 #'
-
-fit_control <- function(verbosity = 1L, catch = FALSE) {
+control_parsnip <- function(verbosity = 1L, catch = FALSE) {
   res <- list(verbosity = verbosity, catch = catch)
   res <- check_control(res)
-  class(res) <- "fit_control"
+  class(res) <- "control_parsnip"
   res
 }
 
 #' @export
-print.fit_control <- function(x, ...) {
+#' @rdname control_parsnip
+fit_control <- function(verbosity = 1L, catch = FALSE) {
+  control_parsnip(verbosity = verbosity, catch = catch)
+}
+
+#' @export
+print.control_parsnip <- function(x, ...) {
   cat("parsnip control object\n")
   if (x$verbosity > 1)
     cat(" - verbose level", x$verbosity, "\n")

R/decision_tree.R

@@ -84,7 +84,7 @@
 #'  reloaded and reattached to the `parsnip` object.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' decision_tree(mode = "classification", tree_depth = 5)
 #' # Parameters can be represented by a placeholder:

R/fit.R

@@ -19,7 +19,7 @@
 #'  outcome(s), predictors, case weights, etc). Note: when needed, a
 #'  \emph{named argument} should be used.
 #' @param control A named list with elements `verbosity` and
-#'  `catch`. See [fit_control()].
+#'  `catch`. See [control_parsnip()].
 #' @param ... Not currently used; values passed here will be
 #'  ignored. Other options required to fit the model should be
 #'  passed using `set_engine()`.
@@ -42,7 +42,7 @@
 #'
 #' If the model engine has not been set, the model's default engine will be used
 #'  (as discussed on each model page). If the `verbosity` option of
-#'  [fit_control()] is greater than zero, a warning will be produced.
+#'  [control_parsnip()] is greater than zero, a warning will be produced.
 #' @examples
 #' # Although `glm()` only has a formula interface, different
 #' # methods for specifying the model can be used
@@ -81,7 +81,7 @@
 #'  The return value will also have a class related to the fitted model (e.g.
 #'  `"_glm"`) before the base class of `"model_fit"`.
 #'
-#' @seealso [set_engine()], [fit_control()], `model_spec`, `model_fit`
+#' @seealso [set_engine()], [control_parsnip()], `model_spec`, `model_fit`
 #' @param x A matrix or data frame of predictors.
 #' @param y A vector, matrix or data frame of outcome data.
 #' @rdname fit
@@ -91,7 +91,7 @@ fit.model_spec <-
   function(object,
            formula = NULL,
            data = NULL,
-           control = fit_control(),
+           control = control_parsnip(),
            ...
   ) {
     if (object$mode == "unknown") {
@@ -182,7 +182,7 @@ fit_xy.model_spec <-
   function(object,
            x = NULL,
            y = NULL,
-           control = fit_control(),
+           control = control_parsnip(),
            ...
   ) {
     object <- check_mode(object, levels(y))

R/linear_reg.R

@@ -98,7 +98,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [varying()], [fit()], [set_engine()]
+#' @seealso [[fit()], [set_engine()]
 #' @examples
 #' linear_reg()
 #' # Parameters can be represented by a placeholder:

R/logistic_reg.R

@@ -97,7 +97,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' logistic_reg()
 #' # Parameters can be represented by a placeholder:

R/mars.R

@@ -55,7 +55,7 @@
 #'  attached.
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' mars(mode = "regression", num_terms = 5)
 #' @export

R/mlp.R

@@ -77,7 +77,7 @@
 #' \Sexpr[results=rd]{parsnip:::show_fit(parsnip:::mlp(mode = "regression"), "nnet")}
 #'
 #' @importFrom purrr map_lgl
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' mlp(mode = "classification", penalty = 0.01)
 #' # Parameters can be represented by a placeholder:

R/multinom_reg.R

@@ -80,7 +80,7 @@
 #'  separately saved to disk. In a new session, the object can be
 #'  reloaded and reattached to the `parsnip` object.
 #'
-#' @seealso [varying()], [fit()]
+#' @seealso [[fit()]
 #' @examples
 #' multinom_reg()
 #' # Parameters can be represented by a placeholder: